We have applied a genetic algorithm to minimization the reproduction error of natural objects
in a calibrated CRT display. The GOG model of calibrated CRT display provides the relative scalar
values (R,G,B) of each channel-colour. With these values the CRT Display reproduces the colour of
natural objects. The reproduced colour in the CRT display is different of the colour of natural object
under the selected illuminant (D65). We have perturbed each relative scalar values RGB with one
increment (ΔR, ΔG, ΔB) to minimize the reproduced colour error. We obtain these increments using a
genetic algorithm. The genetic algorithm uses the relative scalar values (R,G,B) provided by the GOG
model to generate the initial population and the colour difference ΔE94 of CIELAB system as merit
function. We have applied a genetic algorithm to minimization the reproduction error of 24 natural
objects of ColorChecker chart in the calibrated CRT display. The ColorChecker chart is uniform
illuminated with 90π lx of illuminant D65

Arasa Martí, Josep

Martínez-Verdú, Francisco M.

Pizarro Bondia, Carles

Repositorio Institucional de la Universidad de Alicante

AIC Colour 05 - 10th Congress of the International Colour Association  1235 Application of genetic algorithms to minimization of the reproduction error of natural objects in calibrated CRT display  using the GOG model  C. Pizarro1, F. Martínez-Verdú2, J. Arasa1 1 Centro de Desarrollo de Sensores Instrumentación y Sistemas (CD6),                                     Universidad Politécnica de Cataluña (SPAIN) 2Departamento de Óptica, Universidad de Alicante (SPAIN) Corresponding author: C. Pizarro (pizarro@oo.upc.edu)  ABSTRACT  We have applied a genetic algorithm to minimization the reproduction error of natural objects in a calibrated CRT display. The GOG model of calibrated CRT display provides the relative scalar values (R,G,B) of each channel-colour. With these values the CRT Display reproduces the colour of natural objects. The reproduced colour in the CRT display is different of the colour of natural object under the selected illuminant (D65). We have perturbed each relative scalar values RGB with one increment (∆R, ∆G, ∆B) to minimize the reproduced colour error. We obtain these increments using a genetic algorithm. The genetic algorithm uses the relative scalar values (R,G,B) provided by the GOG model to generate the initial population and  the colour difference ∆E94 of CIELAB system as merit function. We have applied a genetic algorithm to minimization the reproduction error of 24 natural objects of ColorChecker chart in the calibrated CRT display. The ColorChecker chart is uniform illuminated with 90π lx of illuminant D65.   1. INTRODUCTION  The GOG (gain-offset-gamma) model describes with high accuracy the tristimulus values that the CRT display reproduces. For this application, the GOG model to CRT display used is:   Channel Gain (g) Offset (o) Gamma (γ) Gain-Offset ecuation Red 1 0 2.5274 g + o = 1 Green  1 0 2.6279 g + o = 1 Blue 1 0 2.8293 g + o = 1      Table 1. Gain-offset-gamma values of the CRT display GOG model   ⎥⎥⎥⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎢⎢⎢⎣⎡⎟⎠⎞⎜⎝⎛⎟⎠⎞⎜⎝⎛⎟⎠⎞⎜⎝⎛⋅⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡8293.26279.25274.222552552555.10156.18576.3046.965.8448.2942.1967.4044.54BGRNDNDNDmcdZYX (1)  where NDk are the digital counts in the display.  We need to apply the inverse problem to reproduce the colour of a natural object under a selected illuminant. In this case, we need to calculate which are the digital counts (NDk) that reproduce the tristimulus values (Xstd ,Ystd ,Zstd) of a natural object under the selected illuminant. The inverse GOG model is used to do this calculation. For our CRT display this model is:  AIC Colour 05 - 10th Congress of the International Colour Association  1236 ⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡⋅⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=⎥⎥⎥⎥⎥⎥⎥⎦⎤⎢⎢⎢⎢⎢⎢⎢⎣⎡⎟⎠⎞⎜⎝⎛⎟⎠⎞⎜⎝⎛⎟⎠⎞⎜⎝⎛=⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡ −stdstdstdBGRZYXNDNDNDBGR 18293.26279.25274.25.10156.18576.3046.965.8448.2942.1967.4044.54255255255 (2)  The relative scalar values RGB for each channel must be between 0 and 1 to reproduce this colour in the CRT display.  The CRT display behaves as an additive reproduction device of the colour; therefore the spectrum of natural objects reproduced in the CRT display is a linear combination of the spectrum of three primary channels of the CRT display, where the scalar values of each channel are the relative scalar values (R, G, B) provided by the inverse GOG model (Eq. 2). The mathematical expression of the spectral colour (C) reproduced in the CRT display is:      )()()()( iBiGiRi PBPGPRC λλλλ ++=  (3)  where λi are the wavelengths from 380 nm to 780 nm and PR, PG, and PB are the three spectral channels of the CRT display.   From the spectral colour (C) in the display we can calculate the tristimulus values of the reproduced colour (Xr ,Yr ,Zr). The mathematical expression to obtain these values in cd/m2 is:   ( ) ( )( ) ( )( ) ( )∑∑∑======⋅=⋅=⋅=780380780380780380*4*683*4*683*4*683iiiiriiiiriiiirzCZyCYxCXλλλλλλ  (4)  We evaluate the reproduction error of CRT display by mean of the colour difference provided by CIELAB system between the tristimulus values (Xstd ,Ystd ,Zstd) of a natural object under a selected illuminant and the tristimulus values (Xr ,Yr ,Zr) of the reproduced colour in the CRT display, using the white colour of the CRT display as the white reference.          2. METHOD   In this work we have applied a genetic algorithm to minimize the reproduction error of natural objects in a calibrated CRT display. For this purpose we have perturbed each relative scalar values RGB with one increment. Now, the mathematical expression of the spectral colour (C) reproduced in the CRT display is:      ( ) ( ) ( ) )()()()( iBiGiRi PBBPGGPRRC λλλλ ∆++∆++∆+=  (5)  Where R, G, B are the relative scalar values provided by the inverse GOG model (Eq. 2) and ∆R, ∆G, ∆B are the values introduced to minimize the reproduction error. To look for the value of these increments we have adapted a genetic algorithm to colour reproduction using the relative scalar values R, G, B for generating the initial population. However, we have used the typical parameter ∆E of CIELAB system used to evaluate the colour difference as merit function. We have applied a genetic algorithm with this quality function described previously to minimization the reproduction error of 24 natural objects of ColorChecker chart in the CRT display. AIC Colour 05 - 10th Congress of the International Colour Association  1237 We can see the ColorChecker chart at Figure 1. The ColorChecker is uniform illuminated with 90π lx of illuminant D65.                                               Figure 1. The ColorChecker chart   3. RESULTS  We have applied a genetic algorithm nctions described previously to minimization the reproduction error of 24 natural objects of ColorChecker chart (Fig. 1) in the CRT display. We can see the spectrum of three primary channels of the CRT display.  In table 2 we can see the relative scalar values R, G, B values provided by the CRT display GOG model. The increment values ∆R, ∆G, ∆B provided by the genetic algorithm. The colour difference ∆E94 of CIELAB system obtained without increment values. The colour difference N-∆E94 of CIELAB system obtained with increment values. For this CRT display the use of increment values (∆R, ∆G, ∆B) reduce de value of the colour different ∆E of CIELAB system for all natural objects around 12%.                                                          Figure 2. The spectrum of three primary channels   of the CRT display        AIC Colour 05 - 10th Congress of the International Colour Association  1238 COLOUR R G B ∆E94 ∆R ∆G ∆B  N-∆E94Dark Skin 0.177 0.080 0.060 29.62 -0.0095 0.0034 0.0067 25.32 Light Skin 0.543 0.299 0.257 31.64 -0.0112 0.0022 0.0073 23.18 Blue Sky 0.154 0.220 0.404 30.69 0.0086 -0.0047 -0.0051 26.06 Foliage 0.110 0.155 0.047 16.20 -0.0021 0.0059 0.0018 14.90 Blue Flower 0.244 0.226 0.514 25.08 0.0037 -0.0048 -0.0026 22.54 Bluish Green 0.180 0.537 0.450 22.86 0.0049 -0.0056 -0.0031 20.01 Orange 0.664 0.166 0.030 58.65 -0.0072 0.0103 0.0145 52.33 Purplish Blue 0.074 0.118 0.462 44.57 0.0056 -0.0031 -0.0082 39.63 Moderate Red 0.495 0.080 0.139 38.84 -0.0063 0.0015 0.0093 35.11 Purple 0.110 0.041 0.191 13.08 0.0018 -0.0037 -0.0018 12.56 Yellow Green 0.399 0.501 0.026 33.89 -0.0049 0.0058 0.0068 29.98 Orange Yellow 0.784 0.311 0.011 58.64 -0.0084 0.0092 0.0177 52.34 Blue 0.036 0.054 0.371 46.00 0.0071 -0.0060 -0.0125 42.31 Green 0.088 0.308 0.059 6.16 -0.0010 0.0012 0.0021 5.53 Red 0.430 0.028 0.059 47.67 -0.0126 0.0045 0.0065 43.97 Yellow 0.839 0.556 -0.019 53.25 -0.0078 -0.0066 0.0218 48.75 Magenta 0.495 0.077 0.361 12.25 -0.0053 0.0032 0.0039 11.56 Cyan -0.017 0.273 0.434 59.94 0.0331 0.0089 -0.0156 54.67 White 0.911 0.885 0.983 3.79 -0.0029 0.0011 0.0001 3.65 Neutral 8 0.603 0.580 0.659 3.44 0.0003 -0.0001 -0.0001 3.28 Neutral 6.5 0.379 0.361 0.410 2.84 0.0002 -0.0002 -0.0001 2.76 Neutral 5 0.206 0.198 0.223 2.32 0.0001 -0.0001 -0.0002 2.22 Neutral 3.5 0.099 0.093 0.106 1.80 -0.0004 0.0001 0.0003 1.72 Black 0.036 0.033 0.038 1.30 -0.0002 0.0001 0.0001 1.29 Table 2. The relative scalar values R, G, B values provided by the CRT display GOG model. The increment values ∆R, ∆G, ∆B provided by the genetic algorithm. The colour difference ∆E94 of CIELAB system obtained without increment values. The colour difference N-∆E94 of CIELAB system obtained with increment values.        4. CONCLUSIONS   We have applied a genetic algorithm to minimization the reproduction error of natural objects in a calibrated CRT display. We have perturbed each relative scalar value RGB provided by The GOG model of calibrated CRT display with one increment (∆R, ∆G, ∆B) to minimize the reproduced colour error. We obtain these increments using a genetic algorithm. The genetic algorithm uses the relative scalar values (R,G,B) provided by the GOG model to generate the initial population and  the colour difference ∆E94 of CIELAB system as merit function. We have applied a genetic algorithm to minimization the reproduction error of 24 natural objects of ColorChecker chart in the calibrated CRT display. The ColorChecker chart is uniform illuminated with 90π lx of illuminant D65. For this CRT display the use of increment values (∆R, ∆G, ∆B) reduce de value of the colour different ∆E of CIELAB system for all natural objects.   References  1. P. Green and L. W. MacDonald, Colour Engineering: Achieving Device Independent Colour (John Wiley & Sons, Chichester, 2002). 2.. L. W. MacDonald, M. R. Luo, Colour Image Science: Exploiting Digital Media. (John Wiley & Sons, Chichester, 2002). 3. B. A. Wandell and D.L. Silverstein, “Digital Color Reproduction”. In S. K. Shevell editor, Science of Color, 2nd ed. (Elsevier, New York, 2003), pp. 281-316. 4. Z.Michalewicz, ‘Genetic Algorithms + data Structures = Evolution Programs’, Springer, 1996. 5. D.E.Goldberg, ’Genetic Algorithms in search, Optimization and Machine Learning’, ed:Addison Wesley Publishing Company, Inc.  

Application of genetic algorithms to minimization of the reproduction error of natural objects in calibrated CRT display using the GOG model

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